| Name | scaling-vin-pytorch JSON |
| Version |
0.0.12
JSON |
| download |
| home_page | None |
| Summary | Scaling Value Iteration Networks |
| upload_time | 2024-09-23 20:02:09 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT License Copyright (c) 2024 Phil Wang Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
artificial intelligence
deep learning
planning
value iteration network
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
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No Travis.
|
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No coveralls.
|
<img src="./vin.png" width="400px"></img>
## Scaling Value Iteration Networks
Exploration into the <a href="https://arxiv.org/abs/2406.08404">Scaling Value Iteration Networks</a> paper, from Schmidhuber's group
## Usage
```python
import torch
from scaling_vin_pytorch import ScalableVIN
scalable_vin = ScalableVIN(
state_dim = 3,
reward_dim = 2,
num_actions = 10
)
state = torch.randn(2, 3, 32, 32)
reward = torch.randn(2, 2, 32, 32)
agent_positions = torch.randint(0, 32, (2, 2))
target_actions = torch.randint(0, 10, (2,))
loss = scalable_vin(
state,
reward,
agent_positions,
target_actions
)
loss.backward()
action_logits = scalable_vin(
state,
reward,
agent_positions
)
```
## Citations
```bibtex
@article{Wang2024ScalingVI,
title = {Scaling Value Iteration Networks to 5000 Layers for Extreme Long-Term Planning},
author = {Yuhui Wang and Qingyuan Wu and Weida Li and Dylan R. Ashley and Francesco Faccio and Chao Huang and J{\"u}rgen Schmidhuber},
journal = {ArXiv},
year = {2024},
volume = {abs/2406.08404},
url = {https://api.semanticscholar.org/CorpusID:270391752}
}
```
```bibtex
@misc{pflueger2018soft,
title = {Soft Value Iteration Networks for Planetary Rover Path Planning},
author = {Max Pflueger and Ali Agha and Gaurav S. Sukhatme},
year = {2018},
url = {https://openreview.net/forum?id=Sktm4zWRb},
}
```
```bibtex
@inproceedings{Tamar2016ValueIN,
title = {Value Iteration Networks},
author = {Aviv Tamar and Sergey Levine and P. Abbeel and Yi Wu and Garrett Thomas},
booktitle = {Neural Information Processing Systems},
year = {2016},
url = {https://api.semanticscholar.org/CorpusID:11374605}
}
```
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"description": "<img src=\"./vin.png\" width=\"400px\"></img>\n\n## Scaling Value Iteration Networks\n\nExploration into the <a href=\"https://arxiv.org/abs/2406.08404\">Scaling Value Iteration Networks</a> paper, from Schmidhuber's group\n\n## Usage\n\n```python\nimport torch\nfrom scaling_vin_pytorch import ScalableVIN\n\nscalable_vin = ScalableVIN(\n state_dim = 3,\n reward_dim = 2,\n num_actions = 10\n)\n\nstate = torch.randn(2, 3, 32, 32)\nreward = torch.randn(2, 2, 32, 32)\n\nagent_positions = torch.randint(0, 32, (2, 2))\n\ntarget_actions = torch.randint(0, 10, (2,))\n\nloss = scalable_vin(\n state,\n reward,\n agent_positions,\n target_actions\n)\n\nloss.backward()\n\naction_logits = scalable_vin(\n state,\n reward,\n agent_positions\n)\n```\n\n## Citations\n\n```bibtex\n@article{Wang2024ScalingVI,\n title = {Scaling Value Iteration Networks to 5000 Layers for Extreme Long-Term Planning},\n author = {Yuhui Wang and Qingyuan Wu and Weida Li and Dylan R. Ashley and Francesco Faccio and Chao Huang and J{\\\"u}rgen Schmidhuber},\n journal = {ArXiv},\n year = {2024},\n volume = {abs/2406.08404},\n url = {https://api.semanticscholar.org/CorpusID:270391752}\n}\n```\n\n```bibtex\n@misc{pflueger2018soft,\n title = {Soft Value Iteration Networks for Planetary Rover Path Planning},\n author = {Max Pflueger and Ali Agha and Gaurav S. Sukhatme},\n year = {2018},\n url = {https://openreview.net/forum?id=Sktm4zWRb},\n}\n```\n\n```bibtex\n@inproceedings{Tamar2016ValueIN,\n title = {Value Iteration Networks},\n author = {Aviv Tamar and Sergey Levine and P. Abbeel and Yi Wu and Garrett Thomas},\n booktitle = {Neural Information Processing Systems},\n year = {2016},\n url = {https://api.semanticscholar.org/CorpusID:11374605}\n}\n```\n",
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